AI advertising standards landscape

AI advertising standards landscape: how AdCP, OpenRTB, MCP, and A2A relate. Comparison of protocols, standards bodies, and their roles in agentic advertising.

The key standards shaping AI-powered advertising are AdCP (agent workflow coordination), OpenRTB (impression auctions), MCP (AI tool calling), and A2A (agent-to-agent communication). Advertising is moving from programmatic (machine-executed auctions) to agentic (AI agents managing full campaign lifecycles), and these protocols define how the pieces connect.

This page maps the landscape: what exists, who maintains it, and how they fit together.

Active protocols

Ad Context Protocol (AdCP)

AdCP defines how AI agents interact with advertising platforms. It covers the full campaign lifecycle: product discovery, media buying, creative generation, audience activation, brand governance, and delivery reporting.

Scope	Agent-level advertising workflows
Transport	MCP (tool calling) or A2A (agent-to-agent)
Maintained by	AgenticAdvertising.org
License	Apache 2.0 (open source)
Current version	3.0 release candidate
Key tasks	get_products, create_media_buy, build_creative, activate_signal

AdCP is transport-agnostic: the same task definitions work over both MCP and A2A. A platform implements once; agents connect via either transport.

OpenRTB

OpenRTB handles real-time impression auctions — the bid request / bid response cycle that powers most programmatic display and video advertising.

Scope	Impression-level transactions
Transport	HTTP POST
Maintained by	IAB Tech Lab
Current version	2.6 / 3.0
Key objects	Bid request, bid response, win notice, billing notice

OpenRTB and AdCP are complementary. OpenRTB handles “should I bid on this impression?”; AdCP handles “create a campaign with this budget and targeting.” See AdCP and OpenRTB for details.

Model Context Protocol (MCP)

MCP defines how AI models call external tools. Developed by Anthropic, it’s the standard for connecting AI assistants to APIs, databases, and services.

Scope	AI tool calling
Transport	JSON-RPC over stdio or SSE
Maintained by	Anthropic
Used by	Claude, Cursor, Windsurf, and other AI assistants

AdCP uses MCP as one of its transport layers. An AdCP MCP server exposes advertising tasks (like get_products or create_media_buy) as tools that any MCP-compatible AI assistant can call.

Agent-to-Agent Protocol (A2A)

A2A defines how autonomous agents communicate with each other. Developed by Google, it enables multi-agent workflows where specialized agents collaborate.

Scope	Agent-to-agent collaboration
Transport	HTTP + JSON-RPC with SSE streaming
Maintained by	Google
Used by	Multi-agent orchestration frameworks

AdCP uses A2A as its other transport layer. In an A2A setup, a buyer agent sends AdCP tasks to a seller agent as structured messages, with support for long-running operations via streaming.

Standards bodies and organizations

Organization	Focus	Key outputs
AgenticAdvertising.org	AI agent advertising standards	AdCP specification, JSON schemas, reference implementations
IAB Tech Lab	Digital advertising standards	OpenRTB, VAST, ads.txt, sellers.json, content taxonomy
Anthropic	AI safety and research	MCP specification
Google	AI and cloud	A2A specification
W3C	Web standards	Privacy Sandbox APIs, Topics API

AgenticAdvertising.org is an independent member organization. It is not a subsidiary or working group of IAB Tech Lab, Anthropic, Google, or any other company. Its members include platform providers, advertisers, agencies, and developers.

Other standards in the ecosystem

Standard	Purpose	Maintained by
VAST / VPAID	Video ad serving and interactive video	IAB Tech Lab
ads.txt / sellers.json	Supply chain transparency	IAB Tech Lab
Open Measurement SDK	Viewability and attention measurement	IAB Tech Lab
Unified ID 2.0	Privacy-preserving identity	The Trade Desk / Prebid
Privacy Sandbox	Cookie-less targeting APIs	Google / W3C

How the layers fit together

These protocols operate at different layers of the stack:

┌──────────────────────────────────────────────┐
│  Strategy layer (AdCP)                       │
│  Campaign planning, budget allocation,       │
│  cross-platform coordination                 │
├──────────────────────────────────────────────┤
│  Transport layer (MCP / A2A)                 │
│  How agents call tools and exchange data     │
├──────────────────────────────────────────────┤
│  Execution layer (OpenRTB / platform APIs)   │
│  Impression-level auctions, ad serving,      │
│  creative rendering                          │
├──────────────────────────────────────────────┤
│  Measurement layer (OMSDK / UID2 / Privacy)  │
│  Viewability, attribution, identity          │
└──────────────────────────────────────────────┘

A typical workflow: an AI agent uses MCP to call AdCP tasks on a publisher’s platform, creating a campaign. The publisher’s ad server uses OpenRTB to execute impression-level delivery. OMSDK measures viewability. ads.txt verifies the supply chain.

What’s changing

Three trends are reshaping how these standards interact:

Agent-mediated buying. Instead of humans navigating dashboards, AI agents will manage campaigns across platforms. This creates demand for standardized agent interfaces — which is what AdCP provides.

Protocol convergence. MCP and A2A are establishing the transport layer for agent communication. Domain-specific protocols like AdCP build on top of them. This mirrors how HTTP became the transport layer and domain-specific APIs built on top.

Vertical specialization. Generic agent protocols (MCP, A2A) handle communication. Vertical protocols handle domain logic. AdCP handles advertising. The same transport-plus-domain pattern may emerge in other verticals as agent adoption grows.

Getting involved

Introduction to AdCP

Understand the protocol architecture and core concepts.

Join AgenticAdvertising.org

Participate in working groups that shape protocol direction.

AdCP and OpenRTB

Detailed comparison of AdCP and OpenRTB — how they complement each other.

Protocol comparison

Technical comparison of MCP and A2A as AdCP transport layers.